"""
This module contains the Location class.
"""

# Will Holmgren, University of Arizona, 2014-2016.

import os
import datetime

import pandas as pd
import pytz
import h5py

from pvlib import solarposition, clearsky, atmosphere, irradiance
from pvlib.tools import _degrees_to_index

class Location:
    """
    Location objects are convenient containers for latitude, longitude,
    timezone, and altitude data associated with a particular
    geographic location. You can also assign a name to a location object.

    Location objects have two timezone attributes:

        * ``tz`` is a IANA timezone string.
        * ``pytz`` is a pytz timezone object.

    Location objects support the print method.

    Parameters
    ----------
    latitude : float.
        Positive is north of the equator.
        Use decimal degrees notation.

    longitude : float.
        Positive is east of the prime meridian.
        Use decimal degrees notation.

    tz : str, int, float, or pytz.timezone, default 'UTC'.
        See
        http://en.wikipedia.org/wiki/List_of_tz_database_time_zones
        for a list of valid time zones.
        pytz.timezone objects will be converted to strings.
        ints and floats must be in hours from UTC.

    altitude : float, default 0.
        Altitude from sea level in meters.

    name : None or string, default None.
        Sets the name attribute of the Location object.

    See also
    --------
    pvlib.pvsystem.PVSystem
    """

    def __init__(self, latitude, longitude, tz='UTC', altitude=0, name=None):

        self.latitude = latitude
        self.longitude = longitude

        if isinstance(tz, str):
            self.tz = tz
            self.pytz = pytz.timezone(tz)
        elif isinstance(tz, datetime.timezone):
            self.tz = 'UTC'
            self.pytz = pytz.UTC
        elif isinstance(tz, datetime.tzinfo):
            self.tz = tz.zone
            self.pytz = tz
        elif isinstance(tz, (int, float)):
            self.tz = tz
            self.pytz = pytz.FixedOffset(tz*60)
        else:
            raise TypeError('Invalid tz specification')

        self.altitude = altitude

        self.name = name

    def __repr__(self):
        attrs = ['name', 'latitude', 'longitude', 'altitude', 'tz']
        return ('Location: \n  ' + '\n  '.join(
            f'{attr}: {getattr(self, attr)}' for attr in attrs))

    @classmethod
    def from_tmy(cls, tmy_metadata, tmy_data=None, **kwargs):
        """
        Create an object based on a metadata
        dictionary from tmy2 or tmy3 data readers.

        Parameters
        ----------
        tmy_metadata : dict
            Returned from tmy.readtmy2 or tmy.readtmy3
        tmy_data : None or DataFrame, default None
            Optionally attach the TMY data to this object.

        Returns
        -------
        Location
        """
        # not complete, but hopefully you get the idea.
        # might need code to handle the difference between tmy2 and tmy3

        # determine if we're dealing with TMY2 or TMY3 data
        tmy2 = tmy_metadata.get('City', False)

        latitude = tmy_metadata['latitude']
        longitude = tmy_metadata['longitude']

        if tmy2:
            name = tmy_metadata['City']
        else:
            name = tmy_metadata['Name']

        tz = tmy_metadata['TZ']
        altitude = tmy_metadata['altitude']

        new_object = cls(latitude, longitude, tz=tz, altitude=altitude,
                         name=name, **kwargs)

        # not sure if this should be assigned regardless of input.
        if tmy_data is not None:
            new_object.tmy_data = tmy_data
            new_object.weather = tmy_data

        return new_object

    @classmethod
    def from_epw(cls, metadata, data=None, **kwargs):
        """
        Create a Location object based on a metadata
        dictionary from epw data readers.

        Parameters
        ----------
        metadata : dict
            Returned from epw.read_epw
        data : None or DataFrame, default None
            Optionally attach the epw data to this object.

        Returns
        -------
        Location object (or the child class of Location that you
        called this method from).
        """

        latitude = metadata['latitude']
        longitude = metadata['longitude']

        name = metadata['city']

        tz = metadata['TZ']
        altitude = metadata['altitude']

        new_object = cls(latitude, longitude, tz=tz, altitude=altitude,
                         name=name, **kwargs)

        if data is not None:
            new_object.weather = data

        return new_object

    def get_solarposition(self, times, pressure=None, temperature=12,
                          **kwargs):
        """
        Uses the :py:func:`pvlib.solarposition.get_solarposition` function
        to calculate the solar zenith, azimuth, etc. at this location.

        Parameters
        ----------
        times : pandas.DatetimeIndex
            Must be localized or UTC will be assumed.
        pressure : None, float, or array-like, default None
            If None, pressure will be calculated using
            :py:func:`pvlib.atmosphere.alt2pres` and ``self.altitude``.
        temperature : None, float, or array-like, default 12

        kwargs
            passed to :py:func:`pvlib.solarposition.get_solarposition`

        Returns
        -------
        solar_position : DataFrame
            Columns depend on the ``method`` kwarg, but always include
            ``zenith`` and ``azimuth``. The angles are in degrees.
        """
        if pressure is None:
            pressure = atmosphere.alt2pres(self.altitude)

        return solarposition.get_solarposition(times, latitude=self.latitude,
                                               longitude=self.longitude,
                                               altitude=self.altitude,
                                               pressure=pressure,
                                               temperature=temperature,
                                               **kwargs)

    def get_clearsky(self, times, model='ineichen', solar_position=None,
                     dni_extra=None, **kwargs):
        """
        Calculate the clear sky estimates of GHI, DNI, and/or DHI
        at this location.

        Parameters
        ----------
        times: DatetimeIndex
        model: str, default 'ineichen'
            The clear sky model to use. Must be one of
            'ineichen', 'haurwitz', 'simplified_solis'.
        solar_position : None or DataFrame, default None
            DataFrame with columns 'apparent_zenith', 'zenith',
            'apparent_elevation'.
        dni_extra: None or numeric, default None
            If None, will be calculated from times.

        kwargs
            Extra parameters passed to the relevant functions. Climatological
            values are assumed in many cases. See source code for details!

        Returns
        -------
        clearsky : DataFrame
            Column names are: ``ghi, dni, dhi``.
        """
        if dni_extra is None:
            dni_extra = irradiance.get_extra_radiation(times)

        try:
            pressure = kwargs.pop('pressure')
        except KeyError:
            pressure = atmosphere.alt2pres(self.altitude)

        if solar_position is None:
            solar_position = self.get_solarposition(times, pressure=pressure)

        apparent_zenith = solar_position['apparent_zenith']
        apparent_elevation = solar_position['apparent_elevation']

        if model == 'ineichen':
            try:
                linke_turbidity = kwargs.pop('linke_turbidity')
            except KeyError:
                interp_turbidity = kwargs.pop('interp_turbidity', True)
                linke_turbidity = clearsky.lookup_linke_turbidity(
                    times, self.latitude, self.longitude,
                    interp_turbidity=interp_turbidity)

            try:
                airmass_absolute = kwargs.pop('airmass_absolute')
            except KeyError:
                airmass_absolute = self.get_airmass(
                    times, solar_position=solar_position)['airmass_absolute']

            cs = clearsky.ineichen(apparent_zenith, airmass_absolute,
                                   linke_turbidity, altitude=self.altitude,
                                   dni_extra=dni_extra, **kwargs)
        elif model == 'haurwitz':
            cs = clearsky.haurwitz(apparent_zenith)
        elif model == 'simplified_solis':
            cs = clearsky.simplified_solis(
                apparent_elevation, pressure=pressure, dni_extra=dni_extra,
                **kwargs)
        else:
            raise ValueError('{} is not a valid clear sky model. Must be '
                             'one of ineichen, simplified_solis, haurwitz'
                             .format(model))

        return cs

    def get_airmass(self, times=None, solar_position=None,
                    model='kastenyoung1989'):
        """
        Calculate the relative and absolute airmass.

        Automatically chooses zenith or apparant zenith
        depending on the selected model.

        Parameters
        ----------
        times : None or DatetimeIndex, default None
            Only used if solar_position is not provided.
        solar_position : None or DataFrame, default None
            DataFrame with columns 'apparent_zenith', 'zenith'.
        model : str, default 'kastenyoung1989'
            Relative airmass model. See
            :py:func:`pvlib.atmosphere.get_relative_airmass`
            for a list of available models.

        Returns
        -------
        airmass : DataFrame
            Columns are 'airmass_relative', 'airmass_absolute'

        See also
        --------
        pvlib.atmosphere.get_relative_airmass
        """

        if solar_position is None:
            solar_position = self.get_solarposition(times)

        if model in atmosphere.APPARENT_ZENITH_MODELS:
            zenith = solar_position['apparent_zenith']
        elif model in atmosphere.TRUE_ZENITH_MODELS:
            zenith = solar_position['zenith']
        else:
            raise ValueError(f'{model} is not a valid airmass model')

        airmass_relative = atmosphere.get_relative_airmass(zenith, model)

        pressure = atmosphere.alt2pres(self.altitude)
        airmass_absolute = atmosphere.get_absolute_airmass(airmass_relative,
                                                           pressure)

        airmass = pd.DataFrame(index=solar_position.index)
        airmass['airmass_relative'] = airmass_relative
        airmass['airmass_absolute'] = airmass_absolute

        return airmass

    def get_sun_rise_set_transit(self, times, method='pyephem', **kwargs):
        """
        Calculate sunrise, sunset and transit times.

        Parameters
        ----------
        times : DatetimeIndex
            Must be localized to the Location
        method : str, default 'pyephem'
            'pyephem', 'spa', or 'geometric'

        kwargs :
            Passed to the relevant functions. See
            solarposition.sun_rise_set_transit_<method> for details.

        Returns
        -------
        result : DataFrame
            Column names are: ``sunrise, sunset, transit``.
        """

        if method == 'pyephem':
            result = solarposition.sun_rise_set_transit_ephem(
                times, self.latitude, self.longitude, **kwargs)
        elif method == 'spa':
            result = solarposition.sun_rise_set_transit_spa(
                times, self.latitude, self.longitude, **kwargs)
        elif method == 'geometric':
            sr, ss, tr = solarposition.sun_rise_set_transit_geometric(
                times, self.latitude, self.longitude, **kwargs)
            result = pd.DataFrame(index=times,
                                  data={'sunrise': sr,
                                        'sunset': ss,
                                        'transit': tr})
        else:
            raise ValueError('{} is not a valid method. Must be '
                             'one of pyephem, spa, geometric'
                             .format(method))
        return result


def lookup_altitude(latitude, longitude):
    """
    Look up location altitude from low-resolution altitude map
    supplied with pvlib. The data for this map comes from multiple open data
    sources with varying resolutions aggregated by Mapzen.

    More details can be found here
    https://github.com/tilezen/joerd/blob/master/docs/data-sources.md

    Altitudes from this map are a coarse approximation and can have
    significant errors (100+ meters) introduced by downsampling and
    source data resolution.

    Parameters
    ----------
    latitude : float.
        Positive is north of the equator.
        Use decimal degrees notation.

    longitude : float.
        Positive is east of the prime meridian.
        Use decimal degrees notation.

    Returns
    -------
    altitude : float
        The altitude of the location in meters.

    Notes
    -----------
    Attributions:

    * ArcticDEM terrain data DEM(s) were created from DigitalGlobe, Inc.,
      imagery and funded under National Science Foundation awards 1043681,
      1559691, and 1542736;
    * Australia terrain data © Commonwealth of Australia
      (Geoscience Australia) 2017;
    * Austria terrain data © offene Daten Österreichs - Digitales
      Geländemodell (DGM) Österreich;
    * Canada terrain data contains information licensed under the Open
      Government Licence - Canada;
    * Europe terrain data produced using Copernicus data and information
      funded by the European Union - EU-DEM layers;
    * Global ETOPO1 terrain data U.S. National Oceanic and Atmospheric
      Administration
    * Mexico terrain data source: INEGI, Continental relief, 2016;
    * New Zealand terrain data Copyright 2011 Crown copyright (c) Land
      Information New Zealand and the New Zealand Government
      (All rights reserved);
    * Norway terrain data © Kartverket;
    * United Kingdom terrain data © Environment Agency copyright and/or
      database right 2015. All rights reserved;
    * United States 3DEP (formerly NED) and global GMTED2010 and SRTM
      terrain data courtesy of the U.S. Geological Survey.

    References
    ----------
    .. [1] `Mapzen, Linux foundation project for open data maps
        <https://www.mapzen.com/>`_
    .. [2] `Joerd, tool for downloading and processing DEMs, Used by Mapzen
        <https://github.com/tilezen/joerd/>`_
    .. [3] `AWS, Open Data Registry Terrain Tiles
        <https://registry.opendata.aws/terrain-tiles/>`_

    """

    pvlib_path = os.path.dirname(os.path.abspath(__file__))
    filepath = os.path.join(pvlib_path, 'data', 'Altitude.h5')

    latitude_index = _degrees_to_index(latitude, coordinate='latitude')
    longitude_index = _degrees_to_index(longitude, coordinate='longitude')

    with h5py.File(filepath, 'r') as alt_h5_file:
        alt = alt_h5_file['Altitude'][latitude_index, longitude_index]

    # 255 is a special value that means nodata. Fallback to 0 if nodata.
    if alt == 255:
        return 0
    # Altitude is encoded in 28 meter steps from -450 meters to 6561 meters
    # There are 0-254 possible altitudes, with 255 reserved for nodata.
    alt *= 28
    alt -= 450
    return float(alt)
